
import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
						from sagemaker import Session, TrainingJobAnalytics
						from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv('TEST_SAGEMAKER'					,		'False'				)				) is not True					,		reason='Skipping test because should only be run when releasing minor transformers version'					,		)
@pytest.mark.usefixtures('sm_env'				)
@parameterized_class(
    [
        {
            'framework': 'pytorch',
            'script': 'run_glue_model_parallelism.py',
            'model_name_or_path': 'roberta-large',
            'instance_type': 'ml.p3dn.24xlarge',
            'results': {'train_runtime': 1_600, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
        {
            'framework': 'pytorch',
            'script': 'run_glue.py',
            'model_name_or_path': 'roberta-large',
            'instance_type': 'ml.p3dn.24xlarge',
            'results': {'train_runtime': 1_600, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
    ]				)
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     List[str]			)			->	Tuple:
					if self.framework == "pytorch":
									subprocess.run(
									    f'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split()			,				encoding='''utf-8'''			,				check=SCREAMING_SNAKE_CASE_			,				)
					assert hasattr(self			,				'''env'''			)
	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	Any:
					# configuration for running training on smdistributed Model Parallel
					lowercase_	    =   {
					    '''enabled''': True,
					    '''processes_per_host''': 8,
					}
					lowercase_	    =   {
					    '''enabled''': True,
					    '''parameters''': {
					        '''microbatches''': 4,
					        '''placement_strategy''': '''spread''',
					        '''pipeline''': '''interleaved''',
					        '''optimize''': '''speed''',
					        '''partitions''': 4,
					        '''ddp''': True,
					    },
					}

					lowercase_	    =   {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

					lowercase_	    =   '''trainer''' if self.script == '''run_glue.py''' else '''smtrainer'''
					# creates estimator
					return HuggingFace(
					    entry_point=self.script			,				source_dir=self.env.test_path			,				role=self.env.role			,				image_uri=self.env.image_uri			,				base_job_name=f'''{self.env.base_job_name}-{instance_count}-smp-{name_extension}'''			,				instance_count=SCREAMING_SNAKE_CASE_			,				instance_type=self.instance_type			,				debugger_hook_config=SCREAMING_SNAKE_CASE_			,				hyperparameters={
					        **self.env.hyperparameters,
					        '''model_name_or_path''': self.model_name_or_path,
					        '''max_steps''': 5_0_0,
					    }			,				metric_definitions=self.env.metric_definitions			,				distribution=SCREAMING_SNAKE_CASE_			,				py_version='''py36'''			,				)
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	str:
					TrainingJobAnalytics(SCREAMING_SNAKE_CASE_			).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''			)




	@parameterized.expand([(1,)]			)
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Dict:
					# create estimator
					lowercase_	    =   self.create_estimator(SCREAMING_SNAKE_CASE_			)

					# run training
					estimator.fit()

					# result dataframe
					lowercase_	    =   TrainingJobAnalytics(estimator.latest_training_job.name			).dataframe()

					# extract kpis
					lowercase_	    =   list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']			)
					lowercase_	    =   list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']			)
					# get train time from SageMaker job, this includes starting, preprocessing, stopping
					lowercase_	    =   (
					    Session().describe_training_job(estimator.latest_training_job.name			).get('''TrainingTimeInSeconds'''			,				9_9_9_9_9_9			)
					)

					# assert kpis
					assert train_runtime <= self.results["train_runtime"]
					assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy			)
					assert all(t <= self.results['''eval_loss'''] for t in eval_loss			)

					# dump tests result into json file to share in PR
					with open(f'''{estimator.latest_training_job.name}.json'''			,				'''w'''			) as outfile:
									json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss}			,				SCREAMING_SNAKE_CASE_			)












"""simple docstring"""
def 							A						(				__snake_case: int = 3 ,     __snake_case: int = 7 ,     __snake_case: int = 1_0_0_0_0_0_0  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    0
       __magic_name__      				=    1

       for current_denominator in range(1 ,     limit + 1  ):
              __magic_name__      				=    current_denominator * numerator // denominator
              if current_denominator % denominator == 0:
                     current_numerator -= 1
              if current_numerator * max_denominator > current_denominator * max_numerator:
                     __magic_name__      				=    current_numerator
                     __magic_name__      				=    current_denominator
       return max_numerator


if __name__ == "__main__":
 print(solution(numerator=3, denominator=7, limit=1_0_0_0_0_0_0))